Enviar pesquisa
Carregar
50120140504022
•
0 gostou
•
284 visualizações
IAEME Publication
Seguir
Tecnologia
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 8
Baixar agora
Baixar para ler offline
Recomendados
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...
paperpublications3
Genetic Approach to Parallel Scheduling
Genetic Approach to Parallel Scheduling
IOSR Journals
40120130405011
40120130405011
IAEME Publication
Software Testing Using Genetic Algorithms
Software Testing Using Genetic Algorithms
IJCSES Journal
Human Resource Recruitment using Fuzzy Decision Making Method
Human Resource Recruitment using Fuzzy Decision Making Method
IJSRD
Optimization technique genetic algorithm
Optimization technique genetic algorithm
Uday Wankar
E034023028
E034023028
ijceronline
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
IOSR Journals
Recomendados
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...
paperpublications3
Genetic Approach to Parallel Scheduling
Genetic Approach to Parallel Scheduling
IOSR Journals
40120130405011
40120130405011
IAEME Publication
Software Testing Using Genetic Algorithms
Software Testing Using Genetic Algorithms
IJCSES Journal
Human Resource Recruitment using Fuzzy Decision Making Method
Human Resource Recruitment using Fuzzy Decision Making Method
IJSRD
Optimization technique genetic algorithm
Optimization technique genetic algorithm
Uday Wankar
E034023028
E034023028
ijceronline
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
IOSR Journals
Genetic Algorithms
Genetic Algorithms
Alaa Khamis, PhD, SMIEEE
Artificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path Planning
iosrjce
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Xin-She Yang
Genetic Algorithms
Genetic Algorithms
anas_elf
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
IRJET Journal
Genetic Algorithms
Genetic Algorithms
adil raja
Genetic algorithm
Genetic algorithm
Megha V
Fuzzy Genetic Algorithm
Fuzzy Genetic Algorithm
Pintu Khan
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
ijseajournal
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
ijaia
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
gerogepatton
I045046066
I045046066
IJERA Editor
Genetic Algorithms and Programming - An Evolutionary Methodology
Genetic Algorithms and Programming - An Evolutionary Methodology
acijjournal
Presentation v2
Presentation v2
MehrnooshV
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
IJCI JOURNAL
Data Science - Part XIV - Genetic Algorithms
Data Science - Part XIV - Genetic Algorithms
Derek Kane
The potential role of ai in the minimisation and mitigation of project delay
The potential role of ai in the minimisation and mitigation of project delay
Pieter Rautenbach
Demonstration1 G As
Demonstration1 G As
Safi Ur Rehman
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
IJCSIS Research Publications
Genetic Algorithm
Genetic Algorithm
Bhushan Mohite
50120130406046
50120130406046
IAEME Publication
M017127578
M017127578
IOSR Journals
Mais conteúdo relacionado
Mais procurados
Genetic Algorithms
Genetic Algorithms
Alaa Khamis, PhD, SMIEEE
Artificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path Planning
iosrjce
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Xin-She Yang
Genetic Algorithms
Genetic Algorithms
anas_elf
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
IRJET Journal
Genetic Algorithms
Genetic Algorithms
adil raja
Genetic algorithm
Genetic algorithm
Megha V
Fuzzy Genetic Algorithm
Fuzzy Genetic Algorithm
Pintu Khan
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
ijseajournal
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
ijaia
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
gerogepatton
I045046066
I045046066
IJERA Editor
Genetic Algorithms and Programming - An Evolutionary Methodology
Genetic Algorithms and Programming - An Evolutionary Methodology
acijjournal
Presentation v2
Presentation v2
MehrnooshV
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
IJCI JOURNAL
Data Science - Part XIV - Genetic Algorithms
Data Science - Part XIV - Genetic Algorithms
Derek Kane
The potential role of ai in the minimisation and mitigation of project delay
The potential role of ai in the minimisation and mitigation of project delay
Pieter Rautenbach
Demonstration1 G As
Demonstration1 G As
Safi Ur Rehman
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
IJCSIS Research Publications
Mais procurados
(19)
Genetic Algorithms
Genetic Algorithms
Artificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path Planning
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Genetic Algorithms
Genetic Algorithms
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
IRJET-In sequence Polemical Pertinence via Soft Enumerating Repertoire
Genetic Algorithms
Genetic Algorithms
Genetic algorithm
Genetic algorithm
Fuzzy Genetic Algorithm
Fuzzy Genetic Algorithm
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...
I045046066
I045046066
Genetic Algorithms and Programming - An Evolutionary Methodology
Genetic Algorithms and Programming - An Evolutionary Methodology
Presentation v2
Presentation v2
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGIC
Data Science - Part XIV - Genetic Algorithms
Data Science - Part XIV - Genetic Algorithms
The potential role of ai in the minimisation and mitigation of project delay
The potential role of ai in the minimisation and mitigation of project delay
Demonstration1 G As
Demonstration1 G As
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
Discovery of Jumping Emerging Patterns Using Genetic Algorithm
Semelhante a 50120140504022
Genetic Algorithm
Genetic Algorithm
Bhushan Mohite
50120130406046
50120130406046
IAEME Publication
M017127578
M017127578
IOSR Journals
Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai Univer...
Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai Univer...
Madhav Mishra
Genetic algorithm
Genetic algorithm
Respa Peter
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
IAEME Publication
Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...
IAEME Publication
Analysis of selection schemes for solving job shop scheduling problem using g...
Analysis of selection schemes for solving job shop scheduling problem using g...
eSAT Journals
Analysis of selection schemes for solving job shop
Analysis of selection schemes for solving job shop
eSAT Publishing House
Two-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential Evolution
Xin-She Yang
Biology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering Optimization
Xin-She Yang
Parallel evolutionary approach paper
Parallel evolutionary approach paper
Priti Punia
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...
AI Publications
Medical diagnosis classification
Medical diagnosis classification
csandit
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
cscpconf
A Genetic Algorithm on Optimization Test Functions
A Genetic Algorithm on Optimization Test Functions
IJMERJOURNAL
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
IDES Editor
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
IDES Editor
Genetic algorithms mahyar
Genetic algorithms mahyar
Mahyar Teymournezhad
Survey on evolutionary computation tech techniques and its application in dif...
Survey on evolutionary computation tech techniques and its application in dif...
ijitjournal
Semelhante a 50120140504022
(20)
Genetic Algorithm
Genetic Algorithm
50120130406046
50120130406046
M017127578
M017127578
Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai Univer...
Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai Univer...
Genetic algorithm
Genetic algorithm
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...
Analysis of selection schemes for solving job shop scheduling problem using g...
Analysis of selection schemes for solving job shop scheduling problem using g...
Analysis of selection schemes for solving job shop
Analysis of selection schemes for solving job shop
Two-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential Evolution
Biology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering Optimization
Parallel evolutionary approach paper
Parallel evolutionary approach paper
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...
Medical diagnosis classification
Medical diagnosis classification
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
A Genetic Algorithm on Optimization Test Functions
A Genetic Algorithm on Optimization Test Functions
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Optimization of Mechanical Design Problems Using Improved Differential Evolut...
Genetic algorithms mahyar
Genetic algorithms mahyar
Survey on evolutionary computation tech techniques and its application in dif...
Survey on evolutionary computation tech techniques and its application in dif...
Mais de IAEME Publication
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME Publication
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
IAEME Publication
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
IAEME Publication
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
IAEME Publication
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
IAEME Publication
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
IAEME Publication
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IAEME Publication
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IAEME Publication
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
IAEME Publication
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
IAEME Publication
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
IAEME Publication
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
IAEME Publication
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
IAEME Publication
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
IAEME Publication
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
IAEME Publication
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
IAEME Publication
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
IAEME Publication
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
IAEME Publication
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
IAEME Publication
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
IAEME Publication
Mais de IAEME Publication
(20)
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
Último
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
naman860154
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
HostedbyConfluent
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
2toLead Limited
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
The Digital Insurer
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Delhi Call girls
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Malak Abu Hammad
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Igalia
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAG
Sujit Pal
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
shyamraj55
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
gurkirankumar98700
Slack Application Development 101 Slides
Slack Application Development 101 Slides
praypatel2
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Anna Loughnan Colquhoun
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
Pooja Nehwal
Último
(20)
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAG
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Slack Application Development 101 Slides
Slack Application Development 101 Slides
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
50120140504022
1.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 202 PERFORMANCE & CONVERGENCE ANALYSIS OF A NOVEL MODEL OF GENETIC ALGORITHM TOWARDS GLOBAL MINIMA Yatin Patadiya1 , M/s Saroj Hiranwal2 1, 2 Computer Science & Engineering, Sri Balaji College of Engineering & Technology, Jaipur, Rajsthan, India ABSTRACT NP is the set of decision problems where the solution can be found in polynomial time by a non-deterministic turing machine & can be verified in polynomial time by deterministic turing machine. The hardest of NP problems are called NP-complete problems. Solving an NP complete problem in deterministic way takes exponential time. Function optimization problems are a class of NP-complete problems. Function optimization is the process of finding absolutely best values of the variables so that value of an objective function becomes optimal. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions using techniques such as inheritance, mutation, selection, and crossover. Two most widely used models of genetic algorithm are Holland model & Common model. Both these models have little difference & generally they work the same way. In this work, we present performance and analysis of genetic algorithms for optimization of test functions. Keywords: Evolutionary Algorithms, Function Optimization, Genetic Algorithm, Global Minima. I. INTRODUCTION NP is the set of decision problems where the solution can be found in polynomial time by a non-deterministic turing machine & can be verified in polynomial time by deterministic turing machine. NP contains many important practical problems, the hardest of which are called NP- complete problems. NP hard problems are the problems whose solutions can not even be verified in polynomial time. Solving an NP problem in deterministic way takes exponential time which can be too large beyond the human imagination such as like hundreds of thousands of years. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
2.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 203 II. FUNCTION OPTIMIZATION Function optimization is the process of finding absolutely best values of the variables so that value of an objective function becomes optimal. Global optimization is a process of finding the absolutely best set of admissible conditions under specified constraints to achieve an objective, assuming both are formulated in mathematical terms. Global optimization problems are a class of NP-complete problems[3] so there is not a single algorithm that solves global optimization problems in polynomial time. Optimization problems can be categorized in several categories depending on the characteristics of problem [17]. Two general categories are continuous optimization and discrete optimization depending upon variables of objective functions are continuous or discrete. Basically function optimization problems are made up of following three parts. • An objective function: It specifies the objective function for which optimization is required to be performed. It includes minimization or maximization functions depending upon problem such as to achieve maximum profit at the minimum cost in organization. • A set of variables: It specifies all the variables which affect the value of the objective function. In organization, the variables might include the amounts of different resources used or the time spent on each activity. • A set of constraints: It indicates the set of rules. The variables can take certain values and they cannot take other values depending on the constraints. In the industry, we cannot have unlimited resources or money, time spent on each activity cannot be negative. Mathematical Formulation: max or min F(x) subject to x ∈ D where D={x : l <= x <= u} subject to gj(x) <= 0, where j=1….J • x ∈ Rn : real n-vector of decision • f: Rn -> R : continuous objective function, • D ⊂ Rn : non-empty set of feasible decisions (a proper subset of Rn); • l and u : explicit, finite lower and upper bounds on x, • g : Rn -> Rm : finite collection of continuous constraint functions (J-vector). The above shown model is called bounded, constraint optimization model. If the 1st condition is relaxed then it becomes unbounded means decision variables can take any value. Relaxation of 2nd condition is known as unconstrained optimization. III. GENETIC ALGORITHM Nature has been great source of inspiration in the various fields of human life since ancient age. Many inventions have been done as per the principals of natural phenomena and models. The story in the computer field is not much different. Researchers are trying to develop intelligence machines and to make them more and more intelligence since 1950s. Conventional deterministic model of von-Neuman fails or gives poor performance in many real world applications like pattern reorganization, classification, clustering, optimization process, design of complex model, etc… But in all these applications bio inspired models of computation like artificial neural network, genetic
3.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 204 algorithm, fuzzy logic, etc… work very well. John Holland along with his colleagues has developed genetic algorithm at the University of Michigan during early 1960s [6]. Genetic algorithms are probabilistic, robust and heuristic search algorithms premised on the evolutionary ideas of natural selection and genetic. Charles Darwin had revealed the process of evolution in the nature during 1850s. According to evolution theory, each organism has to live in highly uncertain environment and has to adapt to new conditions and constraints to survive. In the natural selection process, the fittest one survives and others die off. Fittest organisms are selected for the mating purpose and they produce new child by sexual recombination. Sometimes due to genes deficiency in an offspring, a new child has some characteristics which are not present in the parents. So main aim of each living organism is to survive, to mate and to produce as many offspring as possible. Genetic algorithm follows the same natural phenomenon. More over solving any problem with genetic algorithm, it is required to design different parameters and operators carefully [1][6]. Components of genetic algorithm are described subsequently. • Chromosomes: All living objects are different than other objects of the same type or different types. These differences are due to genetic structure, which is called chromosomes. Chromosomes or individuals are consisting of genes. Genes may contain different possible values depending on the environment & constraints. The encoding process of solution as a chromosome is most difficult aspect of solving any problem using genetic algorithm. • Fitness Function: Fitness function is an evaluation function used to measure how good a chromosome is. Fitness value is assigned to each chromosome by fitness function using their genetic structure and relevant information of the chromosome. Fitness value plays big role because subsequent genetic operators use fitness values to select chromosomes. • Reproduction: During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process. Reproduction methods are roulette wheel selection, tournament selection. • Crossover or recombination works as per the principle of sexual recombination. In biological systems, recombination is a complex process that occurs between male and female of same type. Two chromosomes are physically aligned, breakage occurs at one or more location on each chromosome and homologous chromosome fragments are exchanged before the breaks are repaired. Same concept is also applied in the genetic algorithm. In general, crossover operator recombines two chromosomes so it is also known as recombination. Crossover methods are 1-point, n-point, uniform crossover. • Mutation is a genetic operator used to maintain genetic diversity from one generation of a population to the next. It is analogous to biological mutation. Mutation alters one or more gene values in a chromosome from its initial state. Sometimes due to mutation, the solution may change entirely from the previous solution. IV. ENCODING Encoding is the first step towards genetic algorithm. The structure of a solution vector in any search problem depends on the problem characteristics. It may be possible that, in some problems a solution is a single real value; in some problems it may be a real valued vector specifying dimensions to the problem's parameters whereas in some other problems, a solution may be a strategy or an algorithm for achieving a task. So encoding of solution as a chromosome is generally problem dependent [2]. First the data is encoded with the help of some encoding technique. Then it is given to genetic algorithm. Different types of encoding techniques are available such as binary encoding, gray code encoding, decimal encoding, etc…
4.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 205 V. HOLLAND MODEL This model is originally proposed by John Holland and widely used by many researchers [16] [6]. In this model, crossover and mutation work independent of each other. First crossover is applied on the mating pool and temporary population is created then mutation is applied. Crossover probability Pc decides whether to perform crossover on two randomly selected chromosomes or to copy them directly in the next generation population set. Mutation probability Pm is per gene probability, it decides whether to perform mutation on particular gene or not. Generally crossover probability is high like 0.95, 0.90, 0.8, even more. Mutation probability is commonly low, like 0.01, 0.02, 0.05. Begin gen = 0 Initialize P(gen) While termination_condition not satisfied Begin Evaluate each chromosome in P(gen) /* Reproduction */ for i = 1 to pop_size select 1 chromosome and place it into mating pool M /* Mating Pool M is created*/ /*Crossover*/ for i = 1 to (pop_size) / 2 apply crossover on randomly selected chromosomes from M /*Temporary population C1 is created*/ /*Mutation*/ for i = 1 to pop_size apply mutation on each chromosome of C1 /*Temporary population C2 is created*/ gen = gen + 1 P(gen) = C2 End End Figure 1: Procedure of Holland Model VI. COMMON MODEL In Common model, Instead of applying crossover and mutation in sequence, either one is applied according to probability. It may be possible that many times crossover is applied and then mutation is applied, so first local evolution is done and then mutation is used to explore new points. Mutation probability is same as Holland model, it is per gene probability.
5.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 206 Begin gen = 0 Initialize P(gen) While termination_condition not satisfied Begin Evaluate each chromosome in P(gen) /* Reproduction */ for i = 1 to pop_size select 1 chromosome and place it into mating pool M /* Mating Pool M is created*/ temp = random(0,1) if (temp <= Pc) /*Crossover*/ for i = 1 to (pop_size) / 2 apply crossover on randomly selected chromosomes from M else /*Mutation*/ for i = 1 to pop_size apply mutation on each chromosome of M end if /*Temporary population C1 is created*/ gen = gen + 1 P(gen) = C1 End End Figure 2: Procedure of Common Model VII. NEW MODEL In this work, we propose new model of genetic algorithm. Holland model and Common model has little difference. In new model there is no concept of selection or reproduction. Instead of reproduction phase, it is better to give chance to entire population set to mate. We apply sorting operator. Crossover is applied between ith and i+1th chromosomes in the population set. After crossover, we apply mutation same way as Holland model and mutation probability is per gene probability. After completing one generation, we choose chromosomes such a way that generation gap is less than 1.
6.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 207 VIII. SUCCESS RATIO ANALYSIS Table 1: Models wise success ratio Decimal Encoding Holland Common New 1-Point Uniform 1-Point Uniform 1-Point Uniform Lavy Best 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S.R.% 100 100 100 100 100 100 Easom Best -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 S.R.% 88 72 52 24 100 100 Figure 3: Success Ratio Chart IX. CONVERGENCE ANALYSIS Figure 4: Comparison of convergence rate between Holland model, Common model & new model for Levy’s function 0 25 50 75 100 Levy Easom SuccessRatio(%) Test Problems Holland Common New
7.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 208 Figure 5: Comparison of convergence rate between Holland model, Common model & new model for Easom’s function X. CONCLUSION In this work, we have optimized Lavy & Easom’s function using Holland & Common model. Then we have introduced a new model & optimized same functions using new model. If we look at performance & convergence analysis of these three models then we can say that new model works better than existing Holland & Common models. XI. REFERENCES [1] D. Beasley, R.B. David and R.R. Martin. An overview of genetic algorithms: Part 1, fundamentals. University Computing, 15(2): 58-69, 1933. [2] D. Beasley, R.B. David and R.R. Martin. An overview of genetic algorithms: Part 2, research topics. University Computing, 15(4): 170-181, 1933. [3] C.H. Papadimitriou and K. Steiglitz. Combinatorial Optimization. Prentice-Hall, 1982. [4] D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addision – Wesley, 1989. [5] D.E. Goldberg and K. Deb. A Comparative analysis of selection schemes used in genetic algorithms. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, pages 69-93, California, 1991. Morgan Kaufmann. [6] J.H. Holland, Adaptation in natural and artificial systems. MIIT Press, second edition, 1992 [7] H. Muhlenbein. How Genetic algorithms really work: I. mutation and hillclimbing. In R. Manner and B. Manderick, editors, Problem Solving from Nature – PPSN II, pages 15-25, Amsterdam, 1992. [8] K.A. De Jong and J. Sharma. Generation gaps revisited. In Darrell Whiteley, editor, Foundations of Genetic Algorithms 2, pages 19-28. Morgan Kauffmann, 1992. [9] K.A. De Jong. And W.M. Spears. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and artificial intelligence, 5:1-26, 1992. [10] K.A. De Jong. Genetick Algorithms are not function optimizers. In L. Darrell Whiteley, editor, Foundations of Genetic Algorithms 2, pages 5-17, San Mateo, CA, 1993. Morgan Kaufmann. [11] Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer- Verlag, Berlin, second edition, 1994.
8.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 202-209 © IAEME 209 [12] D.B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, 1995. [13] T. Back, D. Fogel, Z. Michalewicz and S. Pidgeon, editors. Handbook of Evolutionary Computation. Oxford University Press, 1997. [14] L.D. Chambers, editor. Practical Handbook of Genetic Algorithms, volume 3, Complex Coding Systems. CRC Press, Boca Raton, 1999. [15] M. Gen and R. Cheng. Genetic Algorithms and Engineering Optimization. Engineering Design and Automation. Wiley Interscience Publication, John Wiley & Sons. Inc., New York, 2000. [16] M. Mitchell. An Introduction to Genetic Algorithms. Prentice-Hall, New Delhi, India, 2002. [17] P.M. Pardalos and E. Romeijn, editors. Handbook of Global Optimization – Volume 2: Heuristic Approaches. Kluwer Academic Publishers, 2002. [18] T.P. Patalia, Dr. G.R. Kulkarni, Behavioral Analysis of Genetic Algorithm for Function Optimization, published at IEEE International Conference, Coimbatore, 2010. [19] Meera Kapoor, Vaishali Wadhwa, Optimization of DE Jong’s Function Using Genetic Algorithm Approach, IJARECE, Volume 1, Issue 1, July 2012. [20] Kapil Juneja, Nasib Singh Gill, Optimization of Dejong Function using GA under Different Selection Algorithms, International Journal of Computer Applications (0975 – 8887) Volume 64– No.7, February 2013. [21] Sugandhi Midha, “Comparative Study of Remote Sensing Data Based on Genetic Algorithm”, International journal of Computer Engineering & Technology (IJCET), Volume 5, Issue 1, 2014, pp. 141 - 152, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [22] Rakesh Kumar, Dr Piush Verma and Dr Yaduvir Singh, “A Review and Comparison of Manet Protocols with Secure Routing Scheme Developed using Evolutionary Algorithms”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 167 - 180, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
Baixar agora